Our method promises to be a useful tool for curating large medical image datasets for precision medicine and research purposes. A memory-based model using efficient and robust algorithms for 3D keypoint extraction and indexing [1]}, [2]} allows for fine-grained comparisons between \(O(N^2)\)  image pairs in \(O(N~\log ~N)\)  computational complexity. The Jaccard distance may thus be used to rapidly validate relationship labels, e.g. unexpectedly low distance may indicate related individuals and unexpectedly high distance may indicate unrelated individuals or spatially misaligned pairs. Previous work identified mislabelled scans of individuals in large neuroimage datasets [3]}, here our improved method allowed us predict the self-reported families of three subjects labelled as unrelated due to inconclusive genotyping [4]}, on the basis of nearest neighbor soft Jaccard distance. This exceptional result was confirmed by the Human Connectome Project, and serves as a concrete example as to how our method can be used to validate labels associated with medical imaging data.
